San Jose State University

Medical events can affect space crew health and compromise the success of deep space missions. To successfully manage such events, crew members must be sufficiently prepared to manage certain medical conditions for which they are not technically trained. Extended Reality (XR) can provide an immersive, realistic user experience that, when integrated with augmented clinical tools (ACT), can improve training outcomes and provide real-time guidance during non-routine tasks, diagnostic, and therapeutic procedures. The goal of this study was to develop a framework to guide XR platform development using astronaut medical training and guidance as the domain for illustration. We conducted a mixed-methods study—using video conference meetings (45 subject-matter experts), Delphi panel surveys, and a web-based card sorting application—to develop a standard taxonomy of essential XR capabilities. We augmented this by identifying additional models and taxonomies from related fields. Together, this “taxonomy of taxonomies,” and the essential XR capabilities identified, serve as an initial framework to structure the development of XR-based medical training and guidance for use during deep space exploration missions. We provide a schematic approach, illustrated with a use case, for how this framework and materials generated through this study might be employed.

This chapter will chronicle the journey of the community of operators, regulators, and researchers who embarked upon the task of identifying critical human factors of maintenance and inspection, establishing a database and research tools, developing practical strategies for reducing the risks of maintenance and inspection errors, and understanding its critical role in preserving flight safety. We will take a historical perspective, developing a timeline that is largely driven by key events such as accidents and regulatory/government actions and initiatives. Part 1 focuses on how the field of maintenance human factors was built, beginning in the late 1980s. Part 2 describes the development of methods and tools for meeting the maintenance human factors challenges identified. Finally, Part 3 updates the original chapter by focusing on issues that have persisted or emerged since 2010.

Air traffic control (ATC) is a safety critical domain that is essential for the safe and efficient movement of air traffic. It is also highly human-centric, with no physical barriers to prevent aircraft incidents or accidents. Air Traffic Controllers (ATCOs—also referred to as “controllers”) are at the sharp end of this safety critical system. To ensure flight safety, ATCOs are required to maintain a consistently high standard of performance. The potential consequences of poor performance are severe, with high costs and potential loss of life. Due to the essential role of the human operator in maintaining safety and efficiency for airspace users, the application of human factors and associated principles has critical implications for system safety. The application of human factors principles can significantly influence all ATC components, including tool design and validation, human performance support, prediction and prevention of performance decline, hazard and incident analysis, and more. This book chapter provides an introduction to ATC within the context of the overarching system of air traffic management (ATM), and presents real-world examples of the application of human factors principles and the resulting impact in this safety critical domain. This chapter is divided into four sections. The first provides an introductory overview to ATM and ATC. The second focuses more specifically on the air traffic controller, with a particular emphasis on the performance-influencing human factors that are associated with ATCO performance decline and human error. The third section builds on the previous introductory information with real-world, operational examples of human factors applications in the air traffic domain, and highlights the importance of applying human factors principles in ATC. Finally, the fourth section explores the potential future developments and changes in the air traffic control environment, and the associated human factors challenges that must be addressed to continue positively supporting ATC.

Despite the principal role of high-tech clusters in local planning practice and research, their location and sectoral typology at the granular level have been rarely studied. This study explores the location of U.S. high-tech clusters at a micro-scale by employing firm-level data sets and spatial statistics and examines their sectoral typology using market concentration indices in 52 large U.S regions. The majority (80 %) of the 627 tech clusters we identify have multiple dominant tech industries or are specialized in professional services. Furthermore, while clusters form the major regional hubs for the high-tech economy, they are home to a very small share (7 %, on average) of regional population. U.S. regions also have widely diverse spatial patterns of high-tech clusters; although some regions have scattered clusters, the New York and Northern California high-tech booming regions have clusters concentrated in central business districts (CBDs). Last, U.S. high-tech clusters and the overall high-tech economy are strongly shaped by the location and performance of professional services, i.e., consulting, legal, computer, engineering, and architectural services.

This paper describes and provides the data on the regenerated-impedance spectra that is computed from experimental results of electrochemical impedance spectroscopy measurements taken from a commercial Li-ion battery. The empirical impedance data of secondary coin type Li-ion batteries were collected in different states of charge ranging from empty to full state of charge configurations. This approach utilizes only a small seed (ex grano) experimental data set to first build an ensemble of weighted disparate models selected based on performance and non-correlative criteria (“co-modelling”) then second to generate what would be the remaining experimental data synthetically. The “Cooperative Model Framework” demonstrates the efficacy of this approach by assessing the synthetically generated data.

Background Transverse sinus stenosis (TSS) is commonly found in Pulsatile Tinnitus (PT) patients. Vortex flow is prominent in venous sinus with stenosis, and so it is important to determine the distribution and strength of the vortical flow to understand its influence on the occurrence of PT. Methods In this study, by using computational fluid dynamics for hemodynamic analysis in patient-specific geometries based on Magnetic Resonance Imaging (MRI), we have investigated the blood flow within the venous sinus of 16 subjects with PT. We have employed both laminar and turbulent flow models for simulations, to obtain (i) streamlines of velocity distribution in the venous sinus, and (ii) pressure distributions of flow patterns in the venous sinus. Then, hemodynamic analysis in the venous sinus recirculation zone was carried out, to determine the flow patterns at the junction of transverse sinuses and sigmoid sinuses. Finally, we have proposed a new model for turbulence evaluation based on the regression analysis of anatomic and hemodynamics parameters. Results Correlation analysis between the anatomical parameters and the hemodynamic parameters has shown that stenosis at the transverse sinus was the main factor in the local hemodynamics variation in the venous sinus of patients; in this context, it is shown that vorticity can be used as a prime indicator of the severity of the stenosis function. Our results have shown a significant correlation between the vorticity and the stenotic maximum velocity (SMV) (r = 0.282, p = 0.004). Then, a parameterized prediction model is proposed to determine the vorticity in terms of flow and anatomic variables, termed as the turbulence eddy prediction model (TEP model). Our result have shown that the TEP model is sensitive to the dominant flow distribution, with a high correlation to the flow-based vorticity (r = 0.809, p = 0.009). Conclusions The quantification of the vorticity (as both vorticity and MVV) in the downstream of TSS could be a marker for indication of turbulent energy at the transverse-sigmoid sinus, which could potentially serve as a hemodynamic marker for the functional assessment of the PT-related TSS.

This study explores Gen Z diners’ perceptions of restaurant food waste generation and prevention, as well as their related moral decision-making. Drawing on the norm activation model and moral disengagement theory, a dual-route process model was developed to depict Gen Zers’ the moral judgement for wasting food or not at restaurants. Six online focus groups with Gen Z diners in the United States were conducted and thematic analysis was applied. The findings of this study identified multiple underlying psychological mechanisms (e.g., moral obligation activation vs. moral disengagement) for explaining Gen Z diners’ food waste behaviors. Situational factors, cultural factors, and restaurant-related factors all play a key role in the moral judgment process. The findings also revealed what Gen Z diners expect restaurants to do in order to address the food waste problem. This study provides valuable theoretical and managerial implications for tackling the food waste issue. The practical contribution of this study supports the restaurant industry to achieve the UN Sustainable Development Goal 12 “Responsible Consumption and Production”.

Despite growing emphasis on learning to argue and arguing to learn as educational outcomes in the secondary curriculum, a gap remains between these curricular goals and teachers’ practices. The present study seeks to understand this gap by surveying 158 pre-service teachers about their knowledge, beliefs and predispositions related to argumentation and learning. Results reveal a strong, unanimous value for argumentation among pre-service teachers, combined with a vague understanding of what it means to learn and teach argumentation within their academic disciplines. In addition, we found a contradiction between pre-service teachers’ belief in the importance of learning to argue and their reluctance to use instructional time to teach it. Finally, we did not find differences in pre-service teachers’ perceptions across majors. We conclude with recommendations for fostering pedagogical content knowledge in argumentation to pre-service teachers, particularly in content-area methods classes.

The radiation-induced damages in bio-molecules are ubiquitous processes in radiotherapy and radio-biology, and critical to space projects. In this study, we present a precise quantification of the fragmentation mechanisms of deoxyribonucleic acid (DNA) and the molecules surrounding DNA such as oxygen and water under non-equilibrium conditions using the first-principle calculations based on density functional theory (DFT). Our results reveal the structural stability of DNA bases and backbone that withstand up to a combined threshold of charge and hydrogen abstraction owing to simultaneously direct and indirect ionization processes. We show the hydrogen contents of the molecules significantly control the stability in the presence of radiation. This study provides comprehensive information on the impact of the direct and indirect induced bond dissociations and DNA damage and introduces a systematic methodology for fine-tuning the input parameters necessary for the large-scale Monte Carlo simulations of radio-biological responses and mitigation of detrimental effects of ionizing radiation.

The authors review the movement from formative evaluation toward formative assessment. The article explores the emergence of the formative assessment literature out of the authentic, portfolio-based, and alternative assessments reforms in the US context. Offering a set of conceptual frameworks—a characteristics, processes and facet-based perspective and a professional growth over time and developmental trajectories perspective—the authors note that while both perspectives are potentially complementary, the former has tended to yield nominal definitions of and aspirations for formative assessment (FA) practice, whereas the latter has focused attention on modeling teacher growth and exploring developmentally sensitive aspects/levels of FA practice.

Antibacterial suture yarns with antibiotic drugs can be applied to protect wounds; so, they can play an important role in preventing and treating the infection. Engineered sutures are expected to have biomechanical properties, i.e. tensile and viscoelastic behavior, for final applications. This research is aimed to consider the tensile and stress-relaxation behavior of drug loaded and raw hybrid suture yarns with the nano-size structure from a mixture of PolyVinyl Alcohol/ChitoSan (PVA/CS) and Poly Lactic Acid (PLA); this was assessed using the electrospinning method. After optimizing the condition for the fabrication of structure, the selection of the finest diameter was done Then, the drug loaded PLA-PVA/CS nanofiber (PLA/TCH-PVA/CS) with the optimum condition was produced and tetracycline hydrochloride (5% wt) was loaded in the PLA solution; after that, both samples were twisted for manufacturing the suture yarns. FTIR and contact angle analysis were then conducted to characterize the samples. Following that, the tensile and stress relaxation of both samples were tested and simulated by the linear standard solid model, considering the mechanical characteristics of the requested suture. As a general conclusion, the experimental tensile results showed that with loading the drug, stress at 0.3 min and initial curve slopes in the tensile tests were reduced; however, the absolute initial discharge curve slopes values in stress relaxation results were also raised by loading the drug.

BACKGROUND Tunneling nanotubes (TNTs) are cellular structures connecting cell membranes and mediating intercellular communication. TNTs are manually identified and counted by a trained investigator; however, this process is time-intensive. We therefore sought to develop an automated approach for quantitative analysis of TNTs. METHODS We used the convolutional neural network (U-Net) deep learning model to segment phase contrast microscopy images of both cancer and non-cancer cells. Our method was composed of preprocessing and model development. We developed a new preprocessing method to label TNTs on a pixel-wise basis. Two sequential models were employed to detect TNTs. First, we identified the regions of images with TNTs by implementing a classification algorithm. Second, we fed parts of the image classified as TNT-containing into a modified U-Net model to estimate TNTs on a pixel-wise basis. RESULTS The U-Net model detected 73.3% of human expert-identified TNTs, counted TNTs and cells, and calculated the TNT-to-cell ratio (TCR). We obtained a precision of 0.88, recall of 0.67, and f-1 score of 0.76 on a test data set. The predicted and true TCRs were not significantly different between the training and test data sets. CONCLUSIONS In summary, we report application of an automated model generated by deep learning and trained to accurately label and detect TNTs and cells imaged in culture. Continued application and refinement of this process will provide a new approach to the analysis of TNTs, which form to connect cancer and other cells. This approach has the potential to enhance the drug screens intended to assess therapeutic efficacy of experimental agents, and to reproducibly assess TNTs as a potential biomarker of response to therapy in cancer.

The Hubbard model is an essential tool for understanding many-body physics in condensed matter systems. Artificial lattices of dopants in silicon are a promising method for the analog quantum simulation of extended Fermi-Hubbard Hamiltonians in the strong interaction regime. However, complex atom-based device fabrication requirements have meant emulating a tunable two-dimensional Fermi-Hubbard Hamiltonian in silicon has not been achieved. Here, we fabricate 3 × 3 arrays of single/few-dopant quantum dots with finite disorder and demonstrate tuning of the electron ensemble using gates and probe the many-body states using quantum transport measurements. By controlling the lattice constants, we tune the hopping amplitude and long-range interactions and observe the finite-size analogue of a transition from metallic to Mott insulating behavior. We simulate thermally activated hopping and Hubbard band formation using increased temperatures. As atomically precise fabrication continues to improve, these results enable a new class of engineered artificial lattices to simulate interactive fermionic models. Atomically precise artificial lattices of dopant-based quantum dots offer a tunable platform for simulations of interacting fermionic models. By leveraging advances in fabrication and atomic-state control, Wang et al. report quantum simulations of the 2D Fermi-Hubbard model on a 3 × 3 few-dopant quantum dot array.

The human papillomavirus (HPV) is responsible for most cervical cancer cases worldwide. This gynecological carcinoma causes many deaths, even though it can be treated by removing malignant tissues at a preliminary stage. In many developing countries, patients do not undertake medical examinations due to the lack of awareness, hospital resources and high testing costs. Hence, it is vital to design a computer aided diagnostic method which can screen cervical cancer patients. In this research, we predict the probability risk of contracting this deadly disease using a custom stacked ensemble machine learning approach. The technique combines the results of several machine learning algorithms on multiple levels to produce reliable predictions. In the beginning, a deep exploratory analysis is conducted using univariate and multivariate statistics. Later, the one-way ANOVA, mutual information and Pearson's correlation techniques are utilized for feature selection.

To date, research on traumatic stress and treatment-seeking behavior has primarily focused on Western populations. Despite experiencing similar levels of symptomatology, mental health service utilization appears lower among East Asian populations. Stigma toward mental health services may be one barrier to treatment-seeking, especially among individuals who have experienced potentially traumatic events; however, previous research has been primarily conducted in the United States. Less is known about predictors of treatment-seeking attitudes among populations residing in East Asia, particularly college students. The present study examined the relationship between trauma, mental health services stigma, and treatment-seeking attitudes among undergraduate students in Southwestern China. Self-report measures of trauma exposure, posttraumatic stress disorder symptomatology, mental health services stigma, and treatment-seeking attitudes were administered. We hypothesized that students with greater severity of traumatic symptoms would endorse more positive attitudes toward treatment-seeking if they reported lower levels of mental health services stigma. Mental health services stigma was a strong predictor of negative attitudes toward treatment-seeking, whereas neither trauma exposure nor traumatic symptomatology were associated with treatment-seeking attitudes. The significant association between mental health services stigma and treatment-seeking attitudes underscores the importance of destigmatizing mental health to encourage treatment-seeking among the Chinese college student population.

Purpose Cultural responsivity is essential for efficacious and affirming clinical relationships. This may be especially important with historically marginalized clients, such as transgender and gender-diverse (TGD) people seeking behaviorally based affirming communication services. We recommend modifications to standard tools for diagnostics and training that otherwise might undermine our efforts to create an inclusive and affirming environment. Method Modifications to the Rainbow Passage, a standardized paragraph utilized for eliciting speech samples in clinical settings, focused on nongendered terminology and the elimination of content with religious connotations. Results The recommended edits to the Rainbow Passage maintain similar length, cadence, and phonetic balance while considering cultural and health care context for TGD people and other clients. Conclusion Simple linguistic changes to a standardized paragraph maintain clinical benefits and facilitate SLP efforts toward cultural responsivity, client engagement, and good clinical outcomes.

Background The aim of this study was to develop artificial intelligence (AI) guided framework to recognize tooth numbers in panoramic and intraoral radiographs (periapical and bitewing) without prior domain knowledge and arrange the intraoral radiographs into a full mouth series (FMS) arrangement template. This model can be integrated with different diseases diagnosis models, such as periodontitis or caries, to facilitate clinical examinations and diagnoses. Methods The framework utilized image segmentation models to generate the masks of bone area, tooth, and cementoenamel junction (CEJ) lines from intraoral radiographs. These masks were used to detect and extract teeth bounding boxes utilizing several image analysis methods. Then, individual teeth were matched with a patient’s panoramic images (if available) or tooth repositories for assigning tooth numbers using the multi-scale matching strategy. This framework was tested on 1240 intraoral radiographs different from the training and internal validation cohort to avoid data snooping. Besides, a web interface was designed to generate a report for different dental abnormalities with tooth numbers to evaluate this framework’s practicality in clinical settings. Results The proposed method achieved the following precision and recall via panoramic view: 0.96 and 0.96 (via panoramic view) and 0.87 and 0.87 (via repository match) by handling tooth shape variation and outperforming other state-of-the-art methods. Additionally, the proposed framework could accurately arrange a set of intraoral radiographs into an FMS arrangement template based on positions and tooth numbers with an accuracy of 95% for periapical images and 90% for bitewing images. The accuracy of this framework was also 94% in the images with missing teeth and 89% with restorations. Conclusions The proposed tooth numbering model is robust and self-contained and can also be integrated with other dental diagnosis modules, such as alveolar bone assessment and caries detection. This artificial intelligence-based tooth detection and tooth number assignment in dental radiographs will help dentists with enhanced communication, documentation, and treatment planning accurately. In addition, the proposed framework can correctly specify detailed diagnostic information associated with a single tooth without human intervention.

Experimental studies of educational interventions are rarely based on representative samples of the target population. This simulation study tests two formal sampling strategies for selecting districts and schools from within strata when they may not agree to participate if selected: (1) balanced selection of the most typical district or school within each stratum; and (2) random selection. We compared the generalizability of the resulting impact estimates, both to each other and to a stylized approach to purposive selection (the typical approach for experimental studies in education). We found that balanced and random selection of schools within randomly selected districts were the most consistent strategies in terms of generalizability, with minimal difference between the two. Separately, for random selection, we tested two strategies for replacing districts that refused to participate—random and nearest neighbor replacement. Random replacement outperformed nearest neighbor replacement in many, but not all, scenarios. Overall, the findings suggest that formal sampling strategies for selecting districts and schools for experimental studies of educational interventions can substantially improve the generalizability of their impact findings.